A Decision-Theoretic Academic Advisor: Extended Abstract

This paper describes work related to stochastic modeling and decision-theoretic (DT) planning methods applicable to the real-world domain of academic advising. The uncertainty associated with stochastic planning is often approached as a problem of finding a complete mapping from states to actions (a policy). A need to plan for all contingent futures makes (DT) planning an inherently difficult problem in large, real-world domains. This is different from classical planning where the effects of actions are known with certainty, and a step-by-step plan is sufficient for reaching a goal state. Recently, replanning and heuristic planning techniques which operate over determinized (deterministic) versions of stochastic planning problems have seen much success in competitions and in planning literature. This approach typically involves direct use of deterministic solutions in place of a complete policy or as a heuristic for finding a partial or complete policy. These successes have caused some to call into question the types of problems which are suited to DT planning techniques and to examine the role of approximate planning in stochastic domains [Little and Thiébaux, 2007; Sanner, 2008]. Deterministic planning techniques have seen particular success in domains which possess inherently deterministic qualities. Deterministic methods also provide a means for planning in domains which are too large (in terms of size and branching factor) for fast stochastic planning methods. However, current determinization approaches fail on domains with properties such as avoidable dead-end states and multiple goal-reaching paths. Can the success of deterministic planning techniques be extended into domains which exhibit both categories of properties? The domain of academic advising exhibits properties which confound both stochastic planners (size, branching factor) and deterministic planners (avoidable dead-end states, multiple goal-reaching trajectories). Applying stochastic modeling and DT planning to academic advising domains may also provide real-world benefit to students and advisors. The effect and importance of advising services in post-secondary education has been a major topic of discussion in education literature for several decades. Available, quality advising services have been linked to a number of facets of overall student success, satisfaction, and perceived quality and utility of education [King, 1993; Metzner, 1989; Titley and Titley, 1982; Lowe and Toney, 2001]. Despite the importance of academic advising to student success, academic advising services are often found to be inconsistent, and are often not treated as a high priority within the educational community [Titley and Titley, 1982; Lowe and Toney, 2001]. DT planning in this research is in the context of a Markov decision process (MDP) planning model. MDPs are a mathematical formalism used to model a controlled stochastic processes. An MDP is represented by a 4-tuple: 〈S,A, T (s, a, s′), R(s)〉 where S and A are a (finite) set of states and actions, T (s, a, s′) → [0, 1] is a transition function denoting the probability of arriving at state s′ after taking action a from state s, and a real-valued utility function R(s) → R denoting the a reward value for occupying state s.